IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v99y2021ics0140988321001687.html
   My bibliography  Save this article

Technology adoption and carbon emissions with dynamic trading among heterogeneous agents

Author

Listed:
  • Chen, Huayi
  • Ma, Tieju

Abstract

The existing systematic technology adoption models with interacting agents commonly assume that agents function as cooperative entities in a planned economy, with the aim of finding the Pareto optimal solutions of all agents. However, what if the agents are somewhat selfish and act more as entities in a market economy? Little work has been done to explore this question with systematic technology adoption models. This study explores this question by developing a systematic technology adoption model in which each agent intends to minimize its own total cost with the dynamic trading of technology and goods with others, and a Walrasian auction is performed at every time unit to achieve the dynamic trading equilibrium. By using this model, this paper investigates how the dynamic trading among heterogeneous agents affects the adoption of a new advanced technology as well as the corresponding total cost and carbon emissions of the entire system. The previous studies in this modeling stream rarely used applications with real social and economic backgrounds. This study makes a first step by using the model to analyze how the dynamic trading among different regions influences technology adoption, total system costs, and emissions worldwide. The main finding of the study is that dynamic trading among heterogeneous agents is a cost-effective way to accelerate the adoption of a new advanced technology, and to reduce total carbon emissions. Our study implies that global trading is not only related to the economy itself, but can also contribute to technology development and carbon reductions worldwide.

Suggested Citation

  • Chen, Huayi & Ma, Tieju, 2021. "Technology adoption and carbon emissions with dynamic trading among heterogeneous agents," Energy Economics, Elsevier, vol. 99(C).
    • Handle: RePEc:eee:eneeco:v:99:y:2021:i:c:s0140988321001687
    DOI: 10.1016/j.eneco.2021.105263
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988321001687
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2021.105263?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. McPherson, Madeleine & Johnson, Nils & Strubegger, Manfred, 2018. "The role of electricity storage and hydrogen technologies in enabling global low-carbon energy transitions," Applied Energy, Elsevier, vol. 216(C), pages 649-661.
    2. Messner, S. & Golodnikov, A. & Gritsevskii, A., 1996. "A stochastic version of the dynamic linear programming model MESSAGE III," Energy, Elsevier, vol. 21(9), pages 775-784.
    3. Fredrik Hedenus, Christian Azar and Kristian Lindgren, 2006. "Induced Technological Change in a Limited Foresight Optimization Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 109-122.
    4. Sungbae An & Yongsung Chang & Sun-Bin Kim, 2009. "Can a Representative-Agent Model Represent a Heterogeneous-Agent Economy," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(2), pages 29-54, July.
    5. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    6. Azar, Christian & Lindgren, Kristian & Andersson, Bjorn A., 2003. "Global energy scenarios meeting stringent CO2 constraints--cost-effective fuel choices in the transportation sector," Energy Policy, Elsevier, vol. 31(10), pages 961-976, August.
    7. Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.
    8. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    9. Chen, Huayi & Zhou, P., 2019. "Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?," Omega, Elsevier, vol. 89(C), pages 257-270.
    10. Eaves, James & Williams, Jeffrey & Power, Gabriel J., 2016. "Do traders strategically time their pledges during real-world Walrasian auctions?," Journal of Banking & Finance, Elsevier, vol. 71(C), pages 109-118.
    11. Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
    12. Yi Wen, 2009. "When does heterogeneity matter?," Working Papers 2009-024, Federal Reserve Bank of St. Louis.
    13. Valentina Bosetti & Emanuele Massetti & Massimo Tavoni, 2007. "The WITCH Model. Structure, Baseline, Solutions," Working Papers 2007.10, Fondazione Eni Enrico Mattei.
    14. Chi, Chunjie & Ma, Tieju & Zhu, Bing, 2012. "Towards a low-carbon economy: Coping with technological bifurcations with a carbon tax," Energy Economics, Elsevier, vol. 34(6), pages 2081-2088.
    15. Klaassen, Ger & Nentjes, Andries & Smith, Mark, 2005. "Testing the theory of emissions trading: Experimental evidence on alternative mechanisms for global carbon trading," Ecological Economics, Elsevier, vol. 53(1), pages 47-58, April.
    16. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    17. Kanudia, Amit & Loulou, Richard, 1998. "Robust responses to climate change via stochastic MARKAL: The case of Quebec," European Journal of Operational Research, Elsevier, vol. 106(1), pages 15-30, April.
    18. Y. Ermoliev & M. Michalevich & A. Nentjes, 2000. "Markets for Tradeable Emission and Ambient Permits: A Dynamic Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 15(1), pages 39-56, January.
    19. Johnson, Nils & Strubegger, Manfred & McPherson, Madeleine & Parkinson, Simon C. & Krey, Volker & Sullivan, Patrick, 2017. "A reduced-form approach for representing the impacts of wind and solar PV deployment on the structure and operation of the electricity system," Energy Economics, Elsevier, vol. 64(C), pages 651-664.
    20. Martinsen, Dag & Krey, Volker & Markewitz, Peter, 2007. "Implications of high energy prices for energy system and emissions--The response from an energy model for Germany," Energy Policy, Elsevier, vol. 35(9), pages 4504-4515, September.
    21. Alan P. Kirman, 1992. "Whom or What Does the Representative Individual Represent?," Journal of Economic Perspectives, American Economic Association, vol. 6(2), pages 117-136, Spring.
    22. Ma, T. & Grubler, A. & Nakamori, Y., 2009. "Modeling technology adoptions for sustainable development under increasing returns, uncertainty, and heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 195(1), pages 296-306, May.
    23. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    24. Per Krusell & Anthony A. Smith & Jr., 1998. "Income and Wealth Heterogeneity in the Macroeconomy," Journal of Political Economy, University of Chicago Press, vol. 106(5), pages 867-896, October.
    25. Barreto, Leonardo & Kypreos, Socrates, 2004. "Emissions trading and technology deployment in an energy-systems "bottom-up" model with technology learning," European Journal of Operational Research, Elsevier, vol. 158(1), pages 243-261, October.
    26. Leonardo Barreto, Socrates Kypreos, 2002. "Multi-regional technological learning in the energysystems MARKAL model," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 17(3), pages 189-213.
    27. Leibowicz, Benjamin D. & Krey, Volker & Grubler, Arnulf, 2016. "Representing spatial technology diffusion in an energy system optimization model," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 350-363.
    28. Messner, Sabine & Schrattenholzer, Leo, 2000. "MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively," Energy, Elsevier, vol. 25(3), pages 267-282.
    29. Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Huayi & Zhou, P., 2019. "Modeling systematic technology adoption: Can one calibrated representative agent represent heterogeneous agents?," Omega, Elsevier, vol. 89(C), pages 257-270.
    2. Chen, Huayi & Ma, Tieju, 2014. "Technology adoption with limited foresight and uncertain technological learning," European Journal of Operational Research, Elsevier, vol. 239(1), pages 266-275.
    3. Chen, Huayi & Ma, Tieju, 2017. "Optimizing systematic technology adoption with heterogeneous agents," European Journal of Operational Research, Elsevier, vol. 257(1), pages 287-296.
    4. Ma, Tieju & Chen, Huayi, 2015. "Adoption of an emerging infrastructure with uncertain technological learning and spatial reconfiguration," European Journal of Operational Research, Elsevier, vol. 243(3), pages 995-1003.
    5. DeCarolis, Joseph & Daly, Hannah & Dodds, Paul & Keppo, Ilkka & Li, Francis & McDowall, Will & Pye, Steve & Strachan, Neil & Trutnevyte, Evelina & Usher, Will & Winning, Matthew & Yeh, Sonia & Zeyring, 2017. "Formalizing best practice for energy system optimization modelling," Applied Energy, Elsevier, vol. 194(C), pages 184-198.
    6. Keppo, Ilkka & Strubegger, Manfred, 2010. "Short term decisions for long term problems – The effect of foresight on model based energy systems analysis," Energy, Elsevier, vol. 35(5), pages 2033-2042.
    7. Chenhao Fang & Tieju Ma, 2021. "Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price," Annals of Operations Research, Springer, vol. 300(2), pages 577-600, May.
    8. Price, James & Keppo, Ilkka, 2017. "Modelling to generate alternatives: A technique to explore uncertainty in energy-environment-economy models," Applied Energy, Elsevier, vol. 195(C), pages 356-369.
    9. Tieju Ma, 2010. "Coping with Uncertainties in Technological Learning," Management Science, INFORMS, vol. 56(1), pages 192-201, January.
    10. Svensson, Elin & Strömberg, Ann-Brith & Patriksson, Michael, 2011. "A model for optimization of process integration investments under uncertainty," Energy, Elsevier, vol. 36(5), pages 2733-2746.
    11. Chi, Chunjie & Ma, Tieju & Zhu, Bing, 2012. "Towards a low-carbon economy: Coping with technological bifurcations with a carbon tax," Energy Economics, Elsevier, vol. 34(6), pages 2081-2088.
    12. Spalding-Fecher, Randall & Joyce, Brian & Winkler, Harald, 2017. "Climate change and hydropower in the Southern African Power Pool and Zambezi River Basin: System-wide impacts and policy implications," Energy Policy, Elsevier, vol. 103(C), pages 84-97.
    13. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    14. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    15. Jayadev, Gopika & Leibowicz, Benjamin D. & Kutanoglu, Erhan, 2020. "U.S. electricity infrastructure of the future: Generation and transmission pathways through 2050," Applied Energy, Elsevier, vol. 260(C).
    16. Fang, Chenhao & Ma, Tieju, 2020. "Stylized agent-based modeling on linking emission trading systems and its implications for China's practice," Energy Economics, Elsevier, vol. 92(C).
    17. Rout, Ullash K. & Fahl, Ulrich & Remme, Uwe & Blesl, Markus & Voß, Alfred, 2009. "Endogenous implementation of technology gap in energy optimization models--a systematic analysis within TIMES G5 model," Energy Policy, Elsevier, vol. 37(7), pages 2814-2830, July.
    18. Martinsen, Dag & Funk, Carolin & Linssen, Jochen, 2010. "Biomass for transportation fuels--A cost-effective option for the German energy supply?," Energy Policy, Elsevier, vol. 38(1), pages 128-140, January.
    19. Densing, Martin & Turton, Hal & Bäuml, Georg, 2012. "Conditions for the successful deployment of electric vehicles – A global energy system perspective," Energy, Elsevier, vol. 47(1), pages 137-149.
    20. Rout, Ullash K. & Blesl, Markus & Fahl, Ulrich & Remme, Uwe & Voß, Alfred, 2009. "Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model," Energy Policy, Elsevier, vol. 37(11), pages 4927-4942, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:99:y:2021:i:c:s0140988321001687. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.